WO2022126564A1 - Agent intelligent de données polymorphes de soudage par interférence de lumière anti-arc à haute fiabilité et procédé de traitement - Google Patents

Agent intelligent de données polymorphes de soudage par interférence de lumière anti-arc à haute fiabilité et procédé de traitement Download PDF

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Publication number
WO2022126564A1
WO2022126564A1 PCT/CN2020/137415 CN2020137415W WO2022126564A1 WO 2022126564 A1 WO2022126564 A1 WO 2022126564A1 CN 2020137415 W CN2020137415 W CN 2020137415W WO 2022126564 A1 WO2022126564 A1 WO 2022126564A1
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WO
WIPO (PCT)
Prior art keywords
data
intelligent
welding
layer
physical
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Application number
PCT/CN2020/137415
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English (en)
Chinese (zh)
Inventor
冯消冰
刘文龙
Original Assignee
南京水木自动化科技有限公司
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Application filed by 南京水木自动化科技有限公司 filed Critical 南京水木自动化科技有限公司
Priority to PCT/CN2020/137415 priority Critical patent/WO2022126564A1/fr
Publication of WO2022126564A1 publication Critical patent/WO2022126564A1/fr

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
    • B23K37/00—Auxiliary devices or processes, not specially adapted to a procedure covered by only one of the preceding main groups

Definitions

  • the invention relates to the technical field of welding robots, in particular to a highly reliable and anti-arc interference welding polymorphic data intelligent body and a processing method.
  • Welding robots are industrial robots engaged in welding, and have had an important impact on various fields of modern high-tech industries and even people's lives.
  • the main body of the robot travels to the surface of a large steel structure for welding, and is located on the ground or scaffolding.
  • the control device on the robot controls the main body of the robot to complete the welding work.
  • the inventor found that the data generated in the welding process can be used to feedback the product quality defects in the welding robot, and the performance optimization of the welding robot can be fed back through the data to further ensure the welding quality, and finally optimize and iterate a high-quality welding robot.
  • the data generated in the welding process can be used to feedback the product quality defects in the welding robot, and the performance optimization of the welding robot can be fed back through the data to further ensure the welding quality, and finally optimize and iterate a high-quality welding robot.
  • how to manage them effectively is a complicated problem.
  • the purpose of the present invention is to provide a highly reliable anti-arc interference welding polymorphic data agent and processing method, which can process the original welding data of various forms to form usable data that is conducive to analysis, thereby abstracting product quality. question.
  • the present invention provides a welding polymorphic data intelligent body with high reliability and anti-arc interference, including: a main controller, which is used to collect data in real time from various sensing devices of the crawling welding robot, and provides Multiple data interfaces; communication controller, which is connected to the cloud status monitoring device; physical anti-interference isolation layer, which is set between the main controller and the communication controller, and the physical anti-interference isolation layer is used for outer isolation; application layer , which is communicatively connected to the main controller, and the application layer includes an application-layer intelligent master node and an application-layer intelligent standby node that are connected in communication, wherein, when the application-layer intelligent master node fails, the application-layer intelligent master node and the application-layer intelligent standby node State switching is possible, and both the application layer intelligent master node and the application layer intelligent standby node include a composite intelligent body module with a plurality of intelligent algorithms; and a physical entity storage layer, which is communicatively connected to the application layer, and the physical entity storage layer
  • the state switch in the process of entering the cloud synchronization, is changed to the off state. If the crawling welding robot is in the running state at this time, the time consistency verification algorithm will logically verify the normalized morphological data. It is sent to the slice stack backup queue; when the cloud synchronization is completed, the slice stack backup queue is merged into the slice stack queue, and the status switch is changed to the ON state.
  • transforming the image data of the weld through the image feature characterization algorithm includes the following steps: first, the RGB image is subjected to binarization grayscale processing, and then scanned through a plurality of neural nuclei, and the convolution calculation is performed. Low-dimensional data, through multiple low-dimensional processing, finally output several 1*N-dimensional data, and then intercept different segments to represent each data, so that effective features can be extracted from the weld image according to the time dimension and represented as structured multi-dimensional data .
  • the abnormal screening and processing of the original data of the welding machine data and the main push data includes the following steps: filtering the abnormal time series data on the original data of the welding machine data and the main push data, and then finding out all problematic data. A collection of time points, and all data in this collection are eliminated to obtain normalized morphological data.
  • the data collected by the main controller includes image data, welding machine data and machine body motion data.
  • the present invention configures the application layer to include an application layer intelligent master node and an application layer intelligent backup node.
  • Active and standby dual node management the physical entity storage layer is set to include the physical layer storage entity master node and the physical layer storage entity standby node.
  • the welding data is not lost to the greatest extent, and the welding quality data, welding machine data and robot body motion data generated by welding are recorded.
  • Figure 1 is the structure of the highly reliable anti-arc interference welding polymorphic data agent of the preferred embodiment of the present invention
  • FIG. 2 is a flow chart of a method for intelligent processing of welding polymorphic data with high reliability and anti-arc interference according to a preferred embodiment of the present invention.
  • the highly reliable anti-arc interference welding polymorphic data agent includes: a main controller 101, a communication controller 102, a physical anti-interference isolation layer 103, and an application layer 104 and the physical physical storage layer 105.
  • the main controller 101 is used to collect data in real time from various sensing devices of the crawling welding robot 100, and provides multiple data interfaces.
  • the communication controller 102 is in communication connection with the cloud state monitoring device 106 .
  • the physical anti-interference isolation layer 103 is arranged between the main controller 101 and the communication controller 102, and the physical anti-interference isolation layer 103 is used for outer layer isolation.
  • the application layer 104 is in communication connection with the main controller 101, and the application layer 104 includes an application layer intelligent master node 114 and an application layer intelligent standby node 124 that are communicatively connected.
  • the application layer intelligent master node 114 fails, the application layer intelligent master node 114 and the application layer intelligent standby node 124 can perform state switching, thereby quickly realizing the time series data processing and response capabilities of the agent.
  • the application layer intelligent master node 114 and the application layer intelligent standby node 124 each include a composite agent module 134 having a plurality of intelligent algorithms.
  • the physical entity storage layer 105 is in communication connection with the application layer 104.
  • the physical entity storage layer 105 includes a physical layer storage entity master node 115 and a physical layer storage entity backup node 125 that are communicatively connected, and the physical layer storage entity master node 115 and the application layer intelligent master node. 114 Communication connection. Wherein, when the physical layer storage entity master node 115 fails, the physical layer storage entity master node 115 and the physical layer storage entity backup node 125 can make the physical entity storage layer 105 work normally through state switching.
  • a composite agent with multiple intelligent algorithms includes a polymorphic time series data queue, an image feature representation intelligent algorithm body, an anomaly detection intelligent algorithm body, a time check consistency intelligent algorithm body, and an active and standby slice heap processing. body.
  • the physical entity storage layer includes an I/O read/write function and a storage function, and the I/O read/write function requires that the read/write speed is not lower than the frequency of commands issued by the main controller.
  • the storage function satisfies the ability to report twice the failure period in the extreme case of data failure in the cloud, that is, the locally reserved free storage space resources must be guaranteed by 2 times.
  • the physical anti-interference isolation layer is a carbon nanotube (CNT) fiber material, which is used to absorb the electromagnetic radiation of the arc light.
  • CNT carbon nanotube
  • the data collected by the main controller includes image data, welding machine data and machine body motion data
  • the multiple data interfaces include a time series image data interface, a main push time series data interface, a welding time series data interface and a slider time series.
  • the data interface is used to transmit time series images, main push data, welding machine data and slider data respectively.
  • the present invention also discloses a highly reliable anti-arc interference intelligent processing method for welding polymorphic data, including the following steps: constructing a main controller 101, a physical anti-interference isolation layer 103, an application layer 104, a physical entity The storage layer 105 and the communication controller 102 ; the main controller 101 collects data in real time from multiple sensing devices of the crawling welding robot 100 .
  • the main controller 101 transmits the collected data to the composite intelligent body module with multiple intelligent algorithms of the intelligent master node of the application layer through multiple data interfaces.
  • the multiple data interfaces include a time series image data interface, a main push time series data interface, a welding time series data interface and a slider time series data interface, which are respectively used to transmit time series images 201, main push data 202, welding machine data 203 and slider data 204.
  • the composite agent module When the composite agent module receives the time series image 201, the main push data 202, the welding machine data 203 and the slider data 204, the time series image 201, the main push data 202, the welding machine data 203 and the slider data 204 enter the time series queue 211.
  • the image feature characterization intelligent algorithm body 212 converts the image data of the weld through the image feature characterization algorithm, outputs the parameters of the unwelded weld, and outputs the welding quality data that has been welded.
  • the abnormality detection intelligent algorithm body 213 performs abnormality discrimination on the transformed data through the abnormality detection intelligent algorithm, and performs abnormality discrimination and processing on the welding machine data and the original data of the main push data, and obtains normalized morphological data.
  • the temporal consistency checking algorithm body 214 performs logical checking on the normalized morphological data through the temporal consistency checking algorithm, and obtains time-series single morphological data.
  • the time series single morphological data is subjected to heap processing to form extractable time axis playback data.
  • the time axis playback data is subjected to slice stack processing in the slice stack queue 215 and stored in the physical entity storage layer. 105.
  • the cloud state monitoring device 106 periodically obtains the data stored in the physical entity storage layer 105 through the communication controller 102 synchronously.
  • both the application layer and the physical entity storage layer have the dual-node disaster recovery and backup switching function of the active and standby nodes.
  • the application layer intelligent master node fails, the application layer intelligent master node and the application layer intelligent standby node perform state switching; and when the physical layer storage entity master node fails, the physical layer storage entity master node and the physical layer storage entity standby node.
  • the physical physical storage layer works normally through state switching.
  • the state switch 217 in the process of entering the cloud synchronization, is changed to the off state. If the crawling welding robot 100 is in the running state at this time, the time consistency check algorithm normalizes the logic check.
  • the morphological data is sent to the slice stack backup queue 216; when the cloud synchronization is completed, the slice stack backup queue 216 is merged into the slice stack queue 215, and the state switch 217 is changed to the ON state.
  • transforming the image data of the weld through an image feature characterization algorithm includes:
  • the following steps first perform binary grayscale processing on the RGB image, then scan through multiple neural nuclei, calculate low-dimensional data by convolution, and finally output several 1*N-dimensional data through multiple low-dimensional processing, and then separately Different segments are intercepted to represent each data, so that effective features can be extracted from the weld image according to the time dimension and represented as structured multi-dimensional data.
  • abnormal screening and processing are performed on the original data of the welding machine data and the main push data to obtain normalized morphological data, including the following steps: the original data of the welding machine data and the main push data are processed as abnormal time series data. Filter, and then find out all problematic time point sets, and remove all data in this set to obtain normalized morphological data.
  • the welding machine data (such as current and voltage) has a certain reasonable value range, but when it is transmitted through the controller, due to various reasons, there will be abnormal conditions, which exceed reasonable expectations, and obvious error data will appear, so abnormal
  • the detection algorithm will ensure that reasonable data is retained and unreasonable data is deleted through several means:
  • the values of two adjacent time points vibrate violently, with large-span jumps, and they will also be deleted if they exceed a reasonable range. 3.
  • the value of the continuous time period is close to the reasonable upper and lower thresholds. It is necessary to add logical judgment and conditional detection and deletion.
  • the normalized morphological data means that the types of data from different welding machine bodies are inconsistent, and the upper and lower limit value ranges are different, which needs to be unified here. Flatten to 0 ⁇ 1 range, such as voltage/current/main thrust speed, etc.
  • the time consistency check algorithm is based on the data in the interval 0 ⁇ 1 at all the above time points. For example, the result from the image algorithm at 0:01 shows that welding is in progress, but the speed of the main pusher is normal. The normalization value is close to 0. It may be that the time information returned by a certain welding machine component is incorrect, resulting in a time misalignment problem. Therefore, it is necessary to perform time correction again and do a feedback process.
  • the output of the time consistency check algorithm is managed separately according to the dimensions of the different bodies of the welding machine robot.
  • the welding machine has a set of 0 ⁇ 1 data
  • the main pusher has a set of 0 ⁇ 1 data
  • the slider has a set of 0 ⁇ 1 data, all of which are in chronological order.
  • the heap processing is sorted according to the time dimension and regularly compressed (such as every minute) to form a bag. document.
  • the invention ensures the effective management and feedback of the polymorphic data of tens of millions of welding robots in end-testing through the excellent advanced technology of software and hardware, and finally can form the product portrait of the real product of the welding robot, which is used to guide the R&D iteration and precise optimization of the robot.
  • the embodiments of the present application may be provided as a method, a system, or a computer program product.
  • the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects.
  • the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

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  • Physics & Mathematics (AREA)
  • Optics & Photonics (AREA)
  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Manipulator (AREA)
  • Numerical Control (AREA)

Abstract

Son divulgués un agent intelligent de données polymorphes de soudage par interférence de lumière anti-arc à haute fiabilité et un procédé de traitement, l'agent intelligent comportant : un dispositif de commande maître (101), qui est utilisé pour collecter des données en temps réel provenant de divers types de dispositifs de détection d'un robot de soudage rampant (100); un dispositif de commande de communication (102), qui est connecté en communication à un dispositif de surveillance d'état de nuage (106); une couche d'isolation anti-interférence physique (103), qui est disposée entre le dispositif de commande maître (101) et le dispositif de commande de communication (102), la couche d'isolation anti-interférence physique (103) étant utilisée pour fournir une isolation de couche externe; une couche d'application (104), qui est connectée en communication au dispositif de commande maître (101), la couche d'application (104) comprenant un nœud principal intelligent de couche d'application (114) et un nœud en attente intelligent de couche d'application (124) qui sont connectés en communication les uns aux autres; une couche de stockage d'entité physique (105), qui est connectée en communication à la couche d'application (104), la couche de stockage d'entité physique (105) comprenant un nœud principal d'entité de stockage de couche physique (115) et un nœud en attente d'entité de stockage de couche physique (125) qui sont connectés en communication les uns aux autres, le nœud principal d'entité de stockage de couche physique (115) étant connecté en communication au nœud principal intelligent de couche d'application (114).
PCT/CN2020/137415 2020-12-18 2020-12-18 Agent intelligent de données polymorphes de soudage par interférence de lumière anti-arc à haute fiabilité et procédé de traitement WO2022126564A1 (fr)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN202067141U (zh) * 2011-05-27 2011-12-07 浙江师范大学 一种基于s3c2440的电焊机网络管理***
KR101184338B1 (ko) * 2011-03-18 2012-09-20 김상준 레이저 빔 강도의 선형 제어가 가능한 레이저 납땜 장치 및 레이저 용접 장치
CN104625334A (zh) * 2014-12-29 2015-05-20 黑龙江大学 粗管对焊自动控制装置及该控制装置的控制方法
CN111869165A (zh) * 2018-01-22 2020-10-30 西门子股份公司 用于控制和/或监控装置的方法和控制***
CN111940954A (zh) * 2020-08-14 2020-11-17 南京水木自动化科技有限公司 高可靠抗弧光干扰的焊接多形态数据智能体及处理方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101184338B1 (ko) * 2011-03-18 2012-09-20 김상준 레이저 빔 강도의 선형 제어가 가능한 레이저 납땜 장치 및 레이저 용접 장치
CN202067141U (zh) * 2011-05-27 2011-12-07 浙江师范大学 一种基于s3c2440的电焊机网络管理***
CN104625334A (zh) * 2014-12-29 2015-05-20 黑龙江大学 粗管对焊自动控制装置及该控制装置的控制方法
CN111869165A (zh) * 2018-01-22 2020-10-30 西门子股份公司 用于控制和/或监控装置的方法和控制***
CN111940954A (zh) * 2020-08-14 2020-11-17 南京水木自动化科技有限公司 高可靠抗弧光干扰的焊接多形态数据智能体及处理方法

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